Basic rhythmic movements in animals are generated by a network of neurons in the spinal cord called the Central Pattern Generator (“CPG”). Walking, running, swimming, and flying animals have a biological locomotor controller system based on a CPG, which is an autonomous neural circuit generating sustained oscillations needed for locomotion. Naturally-occurring CPGs have been studied and are beginning to be understood. Scientists have studied these naturally-occurring biological CPG systems and, in the early 1900s, articulated the basic notion of such an oscillation-generating autonomous neural circuit for locomotion. T. G. Brown, “On the nature of the fundamental activity of the nervous centres; together with an analysis of the conditioning of the rhythmic activity in progression, and a theory of the evolution of function in the nervous system,” J. Physiol., vol. 48, pp. 18–46 (1914).
An autonomous system of neurons can generate a rhythmic pattern of neuronal discharge that can drive muscles in a fashion similar to that seen during normal locomotion. Locomotor CPGs are autonomous in that they can operate without input from higher centers or from sensors. Under normal conditions, however, these CPGs make extensive use of sensory feedback from the muscles and skin, as well as descending input. A. H. Cohen, S. Rossignol and S. Grillner, Neural Control of Rhythmic Movements in Vertebrates (Wiley & Sons, 1988). Furthermore, the CPG transmits information upward to modulate higher centers as well as to the periphery to modulate incoming sensory information.
The CPG is most often thought of as a collection of distributed elements. For example, in the lamprey (a relatively simple fish-like animal), small, isolated portions of the spinal cord can generate sustained oscillations. When the spinal cord is intact, these small elements coordinate their patterns of activity with their neighbors and over long distances.
It is well known that sensory input can modulate the activity of naturally-occurring, biological CPGs. Modulation of the CPG by sensory input can be seen quite clearly in the adjusting of the phase of the CPG. For example, as a walking cat pushes its leg back, sensors in the leg muscles detect stretching. These sensors (called stretch receptors) signal this stretch to the nervous system. Their firing initiates the next phase of the CPG causing the leg to transition from stance to swing phase.
After some study of naturally-occurring biological CPG systems, scientists modeled CPGs as systems of coupled non-linear oscillators. In the early 1980s Cohen and colleagues introduced a model of the lamprey CPG using a system of phase-coupled oscillators. A. H. Cohen, P. J. Holmes and R. H. Rand, “The nature of the coupling between segmental oscillators of the lamprey spinal generator for locomotion: A mathematical model,” J. Math. Biol., vol. 13, pp. 345–369 (1982). Later, a model called Adaptive Ring Rules (ARR) based on ideas in Cohen et al.'s and related work was extended for use in robot control. M. A. Lewis, Self-organization of Locomotory Controllers in Robots and Animals, Ph.D. dissertation, Dept. of Electrical Engineering, Univ. of Southern Calif., Los Angeles (1996). An ARR is a model of a non-linear oscillator at a behavioral level. Complex enough to drive a robot, an ARR model also allows relatively easy implementation of learning rules.
Certain conventional non-biological (i.e., modeled) CPG-type chips and circuits have been developed. For example, Still reported on a VLSI implementation similar to a CPG circuit used to drive a small robot. S. Still, Presentation at Neurobots Workshop, NIPS*87, Breckenridge, Colo., U.S.A. (1998); S. Still and M. W. Tilden, “Controller for a four legged walking machine, in Neuromorphic Systems: Engineering Silicon from Neurobiology, Eds: L. S. Smith and A. Hamilton (World Scientific, Singapore), pp. 138–148 (1998). Still et al.'s circuit captured some basic ideas of a CPG, and Still's group demonstrated rudimentary control of a walking machine. However, Still's system has no motoneuron output stage, and cannot respond to or adapt based on sensory input.
Ryckebusch and colleagues created a VLSI CPG chip based on observations in the thoracic circuits controlling locomotion in locusts. S. Ryckebusch, M. Wehr, and G. Laurent, “Distinct Rhythmic Locomotor Patterns Can Be Generated by a Simple Adaptive Neural Circuit: Biology, Simulation and VLSI Implementation,” J. of Comp. Neuro., vol. 1, pp. 339–358 (1994). The resulting VLSI chip was used as a fast simulation tool to explore understanding of the biological system. Their system is not a robotic system and cannot respond to or use feedback from sensors.
DeWeerth and colleagues have captured certain neural dynamics on a detailed level. G. Patel, J. Holleman, and S. DeWeerth, “Analog VLSI Model of Intersegmental Coordination with Nearest-Neighbor Coupling,” in Adv. Neural Information Processing, vol. 10, pp. 719–725 (1998). This system of DeWeerth's cannot easily be applied to control a robot, primarily because parameter sensitivity makes such circuits difficult to tune. To address this difficulty, DeWeerth et al. more recently have implemented neurons that self-adapt their firing-rate. M. Simoni, and S. DeWeerth, “Adaptation in a VLSI Model of a Neuron,” in: Trans. Circuits and Systems II, vol. 46, no. 7, pp. 967–970 (1999). The adapted DeWeerth system, however, is not adaptive and does not use external inputs from sensors.
U.S. Pat. No. 5,124,918, entitled “Neural-based autonomous robotic system” to Beer et al., teaches a system for controlling a walking robot using a rhythmic signal used to coordinate locomotion with multiple legs. Beer et al.'s neural-based approach, using software, is relatively basic and does not teach, for example, VLSI implementation, self-adaptation to an environment, low-power compact implementation using one chip, or silicon learning.
In recent years, robotics has been developing in many aspects, of which some have been mentioned above. Also, other challenges have been identified and are being studied in robotics, such as the miniaturization of walking, running, and flying robots, increasing the real-time adaptability of robots to the environment, and the creation of mass-market consumer devices. With these new robotics technologies come demands for smaller, lower-cost, more power-efficient, more adaptive controllers, and, correspondingly, computational support.
Robotics has largely relied for computational support on microprocessor-based technology. Such systems have limitations, such as being unable to provide self-adaptive features.
Not necessarily connected with robotics technologies, a field of neuromorphic engineering developed. Neuromorphic engineering uses principles of biological information processing to address real-world problems, constructing neuromorphic systems from silicon, the physics of which in many ways resembles the biophysics of the nervous system. Neuromorphic engineering to date mostly has concentrated on sensory processing, for example, the construction of silicon retinas or silicon cochleas.
Thus, interesting and exciting advances have been made in the thus-far relatively separate technologies of robotics and neuromorphic engineering. However, work remains to be done to practically bring together these technologies. Conventional robotics and related systems are non-adaptive to their environments, and theoretically the potential exists for huge advances in the direction of increased adaptiveness to the environment. Advanced robotics systems, control of mechanical limbs, control of biological limbs, rhythmic movement in biological systems, have been desired, such as increased responsiveness and relatedness to the environment.